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Snapshot · OMB 2025 inventory · processed Oct 11, 2026
Federal AI Intelligence · U.S. Government AI Observatory

Custom inventory report

Filter: agency = DOL; AI type = classical_ml

Generated 2026-10-12 17:18 UTCDataset: 2025 Federal Agency AI Use Case Inventory (individually reported use cases), Office of Management and Budget (OMB). Version 2025-inventory@62d5baa65fa3, processed 2026-10-11, rules 2026.10.1. Static snapshot.Applied filters: agency = DOL; AI type = classical_ml · 14 records

Key findings

  • 14 use-case records from 1 agencies: 12 deployed (86%), 1 pilots, 1 in development or acquisition, 0 retired, 0 with no stage reported.
  • 0 records are designated high-impact; 0 of them (n/a) answered at least one minimum-practice question.
  • 0% of reported deployed use cases (0 of 12) are classified as generative or agentic AI.
  • Sourcing is reported for 100% of use cases. Where reported, 100% involve in-house development.

Lifecycle

StageRecordsShare
Deployed1286%
Pilot17%
Pre-deployment17%

AI technology

ClassificationRecordsShareDeployed
Classical / predictive ML14100%12

Agencies

AgencyRecordsDeployedHigh-impactCompleteness
DOL · Department of Labor14120100%

High-impact designation

DesignationRecords
Not high-impact14

Governance evidence: use cases designated high-impact

PracticeIn placeIn progressNot in placeN/A or precludedOtherNo answerIn scope
Pre-deployment testing0000000
AI impact assessment0000000
Independent review0000000
Ongoing monitoring0000000
Operator training0000000
Fail-safe0000000
Appeal process0000000
User and public consultation0000000

"In place" means the agency reported the practice as established. "No answer" means the field was blank; it says nothing about whether the practice exists.

Method

All figures are computed directly from the normalised inventory records. Category labels are normalised from the source answer options; original values are kept on each record. Governance answers are classified as in place, in progress, not in place, not applicable, precluded, waived, other or no answer, and blanks are never read as "no". See the Methodology page for full rules.

Limitations

  • Figures are what agencies reported in the 2025 inventory. They are not verified, and registration of use cases is not complete for every agency.
  • A blank answer is missing evidence, not evidence that a practice or safeguard is absent.
  • Counts are use-case records. A larger portfolio does not indicate better performance or greater AI maturity, and agencies differ in how they split systems into use cases.
  • The inventory has no budget, cost, accuracy or outcome data. None is shown or inferred here.
  • This is a disclosure analysis. It is not an audit, a compliance determination or an official government assessment.

Source records

All 14 records.

RecordAgency IDAgencyUse caseStage
R1289DOL-19DOLOEWS Occupation AutocoderDeployed
R1290DOL-20DOLScanner Data Product ClassificationDeployed
R1291DOL-21DOLExpenditure Classification AutocoderDeployed
R1296DOL-27DOLWorker PLUS Microsimulation ProgramDeployed
R1297DOL-28DOLComputer-Assisted Coding: SOII AutocoderDeployed
R1298DOL-29DOLCFOI Record MatchingDeployed
R1301DOL-35DOLCPS OTC PredictionDeployed
R1302DOL-36DOLSample Refinement: Frame APIDeployed
R1303DOL-37DOLCE Interview Item Code EstimationDeployed
R1304DOL-38DOLCE Interview ImputationsDeployed
R1306DOL-40DOLComment Actionability Likelihood ScoreDeployed
R1308DOL-42DOLPPI Price Tolerance PredictionDeployed
R1305DOL-39DOLQCEW NAICS AutocoderPilot
R1307DOL-41DOLComputer-Assisted Review: ORS AutocoderPre-deployment

Prepared with Federal AI Intelligence by Irakli Petriashvili. Source: https://github.com/ombegov/2025-Federal-Agency-AI-Use-Case-Inventory